Incomplete data can lead to biased results and misinterpretations, which can adversely affect public health policies and interventions. For instance, missing demographic information such as age, gender, or geographical location can skew the understanding of disease prevalence and risk factors. Ensuring completeness helps in making accurate epidemiological predictions and formulating effective prevention strategies.
